Ph.D. Theses

Mapping with Limited Sensing

By Kristopher Beevers
Advisor: Wesley Huang
January 15, 2007

For a mobile robot to interact with its environment in complex ways,
it must possess a model of the environment, i.e., a map. It is
generally infeasible for an accurate map to be constructed by hand and
provided to the robot prior to deployment, so the robot must build a
map online.

Most mapping algorithms assume the availability of frequent, high
fidelity feedback from the environment acquired using sensors such as
scanning laser rangefinders. Such sensors provide dense, accurate,
and long-range data at great expense: they typically cost thousands of
(US) dollars, consume significant power and space resources, and
require the use of powerful computers for data processing. These
sensors are therefore unsuitable for deployment in many applications,
e.g., consumer robots, disposable robots for search and rescue or
hazardous material detection, or mobile sensor networks.

This thesis instead examines the mapping problem in the context of
limited sensing. Sensors such as infrared rangefinder arrays are
inexpensive, low-power, and small, but they give only low-resolution,
short-range feedback about the environment and are thus difficult to
use for mapping. The main contributions of this thesis are a
theoretical characterization of the relative capabilities of mapping
sensors, and the development and demonstration of several practical
algorithms for building maps with real, inexpensive sensors.

The thesis begins by examining a generalized model of mapping sensors.
The model is applied with a simple mapping algorithm to characterize
the space of sensors in terms of their suitability for mapping, and
theoretical bounds on map error in terms of the capabilities of the
sensor are derived. The thesis then turns to practical algorithms for
simultaneous localization and mapping (SLAM) with limited sensors. In
the context of particle filtering SLAM, an approach is formulated for
accumulating low-resolution sensor measurements in "multiscans" that
contain sufficient information for feature extraction and data
association. To manage uncertainty due to limited feedback, a new
technique for incorporating prior knowledge in the mapping process is
developed, and new sampling techniques to combat estimation
degeneracies in particle filtering SLAM are described. The algorithms
presented in the thesis are validated experimentally through
simulation, tests on well-known benchmark data sets, and
implementation and deployment on real robots, and a working
implementation of particle filtering SLAM on an 8-bit microcontroller
with a small infrared rangefinder array is described in detail.